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        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        About MultiQC

        This report was generated using MultiQC, version 1.9

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        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

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        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        These samples were run by seq2science v0.2.2, a tool for easy preprocessing of NGS data.

        Take a look at our docs for info about how to use this report to the fullest.

        Contact E-mail
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        Date
        August 27, 2020

        Report generated on 2020-08-27, 11:54 based on data in:

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        General Statistics

        Showing 112/112 rows and 15/37 columns.
        Sample Name% BP Trimmed% Dups% GCLengthInsert Size% Dups% MappedM Total seqs% Proper PairsM Total seqs% AssignedGenome coverageM Genome readsM MT genome readsTreatment Redundancy
        DRR138971
        2.6%
        26.9%
        49%
        50 bp
        74 bp
        4.2%
        75.8%
        149.2
        98.6%
        72.0
        15.0%
        6.3 X
        92.8
        20.3
        0.11
        DRR138971_R2
        2.6%
        26.1%
        49%
        50 bp
        DRR138972
        2.3%
        27.8%
        48%
        50 bp
        74 bp
        3.6%
        84.9%
        124.0
        98.5%
        68.0
        14.8%
        5.9 X
        87.6
        17.6
        0.10
        DRR138972_R2
        2.3%
        27.1%
        48%
        50 bp
        DRR138973
        2.5%
        25.2%
        49%
        50 bp
        74 bp
        3.7%
        78.9%
        113.5
        98.5%
        59.2
        15.4%
        5.2 X
        76.1
        13.4
        0.10
        DRR138973_R2
        2.5%
        24.4%
        49%
        50 bp
        DRR138974
        2.7%
        31.1%
        52%
        50 bp
        74 bp
        4.8%
        54.9%
        124.5
        97.3%
        38.5
        11.9%
        3.3 X
        49.3
        19.1
        0.09
        DRR138974_R2
        2.6%
        26.0%
        52%
        50 bp
        DRR138975
        2.9%
        29.1%
        51%
        50 bp
        72 bp
        4.7%
        58.0%
        86.8
        97.7%
        30.2
        11.9%
        2.6 X
        38.7
        11.6
        0.08
        DRR138975_R2
        2.8%
        24.5%
        51%
        50 bp
        DRR138976
        2.7%
        27.9%
        50%
        50 bp
        74 bp
        3.8%
        67.2%
        119.6
        97.6%
        49.3
        11.2%
        4.3 X
        63.2
        17.2
        0.09
        DRR138976_R2
        2.6%
        23.5%
        50%
        50 bp
        DRR138977
        3.3%
        32.1%
        48%
        48 bp
        72 bp
        5.5%
        81.6%
        188.3
        99.2%
        92.8
        14.0%
        7.9 X
        119.5
        34.1
        0.13
        DRR138977_R2
        3.3%
        31.4%
        48%
        48 bp
        DRR138978
        2.1%
        39.5%
        49%
        49 bp
        76 bp
        10.0%
        80.3%
        216.7
        99.3%
        106.5
        23.8%
        9.0 X
        134.7
        39.5
        0.22
        DRR138978_R2
        2.1%
        38.6%
        49%
        49 bp
        DRR138979
        2.6%
        32.6%
        49%
        49 bp
        74 bp
        8.9%
        78.4%
        204.9
        99.2%
        107.0
        23.5%
        9.0 X
        135.5
        25.0
        0.21
        DRR138979_R2
        2.6%
        31.8%
        49%
        49 bp
        DRR138980
        3.2%
        33.9%
        49%
        48 bp
        67 bp
        5.8%
        81.8%
        112.7
        98.3%
        55.1
        27.1%
        4.6 X
        69.9
        22.2
        0.16
        DRR138980_R2
        3.2%
        33.6%
        49%
        48 bp
        DRR138981
        3.1%
        33.2%
        49%
        48 bp
        69 bp
        5.0%
        80.2%
        115.2
        97.9%
        55.2
        24.0%
        4.6 X
        70.2
        22.2
        0.14
        DRR138981_R2
        3.1%
        32.8%
        49%
        48 bp
        DRR138982
        2.5%
        42.0%
        50%
        49 bp
        73 bp
        8.2%
        76.3%
        145.5
        99.1%
        63.2
        32.9%
        5.2 X
        78.1
        32.9
        0.22
        DRR138982_R2
        2.5%
        41.8%
        50%
        49 bp
        DRR138983
        3.5%
        40.4%
        49%
        48 bp
        64 bp
        9.2%
        86.2%
        151.4
        98.3%
        74.0
        37.7%
        6.1 X
        93.4
        37.1
        0.21
        DRR138983_R2
        3.5%
        40.0%
        49%
        48 bp
        DRR138984
        3.5%
        41.0%
        48%
        48 bp
        66 bp
        8.6%
        93.1%
        121.8
        99.2%
        67.0
        45.0%
        5.4 X
        82.5
        30.8
        0.24
        DRR138984_R2
        3.5%
        40.6%
        48%
        48 bp
        DRR138985
        3.7%
        41.0%
        49%
        48 bp
        63 bp
        7.8%
        79.7%
        155.7
        99.1%
        68.2
        32.4%
        5.7 X
        86.9
        37.3
        0.18
        DRR138985_R2
        3.7%
        40.6%
        49%
        48 bp
        DRR138986
        3.2%
        43.9%
        48%
        48 bp
        64 bp
        9.2%
        93.0%
        124.8
        99.2%
        62.7
        36.7%
        5.2 X
        78.9
        37.2
        0.21
        DRR138986_R2
        3.3%
        43.8%
        48%
        48 bp
        DRR138987
        4.6%
        33.1%
        50%
        48 bp
        61 bp
        6.6%
        79.5%
        88.5
        99.2%
        41.8
        39.7%
        3.4 X
        51.7
        18.6
        0.18
        DRR138987_R2
        4.6%
        32.8%
        50%
        48 bp
        DRR138988
        3.3%
        48.7%
        48%
        48 bp
        66 bp
        10.3%
        93.0%
        171.3
        99.2%
        80.8
        37.6%
        6.7 X
        101.4
        58.0
        0.24
        DRR138988_R2
        3.3%
        48.5%
        48%
        48 bp
        DRR138989
        2.3%
        45.9%
        46%
        49 bp
        73 bp
        9.4%
        97.4%
        117.7
        99.3%
        63.3
        32.7%
        5.3 X
        79.2
        35.5
        0.22
        DRR138989_R2
        2.3%
        45.2%
        46%
        49 bp
        DRR138990
        4.1%
        40.4%
        45%
        48 bp
        59 bp
        5.0%
        97.1%
        63.3
        99.1%
        31.1
        20.1%
        2.7 X
        41.0
        20.5
        0.10
        DRR138990_R2
        4.1%
        39.9%
        45%
        48 bp
        DRR138991
        3.9%
        39.3%
        45%
        48 bp
        65 bp
        7.1%
        96.8%
        91.5
        99.1%
        48.3
        21.9%
        4.1 X
        63.4
        25.2
        0.14
        DRR138991_R2
        3.8%
        38.3%
        45%
        48 bp
        DRR138995
        2.8%
        36.9%
        52%
        50 bp
        73 bp
        6.9%
        56.0%
        205.4
        98.6%
        64.9
        12.1%
        5.6 X
        83.1
        32.0
        0.13
        DRR138995_R2
        2.8%
        35.9%
        52%
        50 bp
        DRR138996
        3.0%
        34.9%
        52%
        49 bp
        72 bp
        6.5%
        59.0%
        141.5
        99.0%
        50.1
        12.1%
        4.3 X
        64.2
        19.2
        0.12
        DRR138996_R2
        3.0%
        33.6%
        52%
        49 bp
        DRR138997
        2.8%
        33.5%
        50%
        50 bp
        74 bp
        5.6%
        68.4%
        196.6
        99.0%
        82.5
        11.4%
        7.2 X
        105.7
        28.8
        0.13
        DRR138997_R2
        2.7%
        32.2%
        50%
        50 bp
        DRR138998
        3.2%
        25.9%
        49%
        48 bp
        67 bp
        2.3%
        81.8%
        25.6
        98.3%
        12.5
        26.9%
        1.0 X
        15.9
        5.1
        0.06
        DRR138998_R2
        3.2%
        25.5%
        49%
        48 bp
        DRR138999
        3.0%
        24.2%
        49%
        48 bp
        69 bp
        1.8%
        80.2%
        21.1
        98.0%
        10.1
        23.7%
        0.8 X
        12.8
        4.1
        0.04
        DRR138999_R2
        3.0%
        23.6%
        49%
        48 bp
        DRR139001
        4.1%
        44.7%
        45%
        48 bp
        60 bp
        9.0%
        97.1%
        137.4
        99.1%
        67.6
        20.2%
        5.8 X
        89.1
        44.4
        0.16
        DRR139001_R2
        4.1%
        44.4%
        45%
        48 bp
        DRR139002
        4.5%
        35.0%
        46%
        48 bp
        59 bp
        4.9%
        96.7%
        49.4
        99.2%
        26.3
        22.6%
        2.2 X
        34.7
        13.1
        0.10
        DRR139002_R2
        4.5%
        34.2%
        46%
        48 bp
        GSM2837499
        18.5%
        28.7%
        51%
        61 bp
        55 bp
        9.3%
        83.2%
        156.8
        93.6%
        73.6
        30.2%
        8.3 X
        99.9
        30.6
        0.21
        GSM2837499_R2
        18.5%
        26.6%
        51%
        61 bp
        GSM2837500
        19.1%
        19.5%
        50%
        60 bp
        54 bp
        7.4%
        87.6%
        141.1
        93.8%
        79.8
        23.7%
        9.1 X
        109.8
        14.0
        0.17
        GSM2837500_R2
        19.0%
        17.9%
        50%
        60 bp
        GSM2837501
        0.7%
        34.2%
        49%
        49 bp
        72 bp
        7.5%
        87.4%
        162.9
        96.9%
        89.1
        24.5%
        7.7 X
        116.9
        25.4
        0.20
        GSM2837501_R2
        0.9%
        33.9%
        49%
        49 bp
        GSM2837502
        19.7%
        22.5%
        51%
        60 bp
        54 bp
        10.0%
        79.3%
        163.6
        94.7%
        84.9
        24.5%
        9.2 X
        111.8
        18.1
        0.21
        GSM2837502_R2
        19.7%
        20.6%
        51%
        60 bp
        GSM2837503
        0.8%
        27.1%
        50%
        49 bp
        111 bp
        4.8%
        86.4%
        96.1
        97.6%
        62.8
        45.9%
        5.1 X
        76.9
        6.1
        0.22
        GSM2837503_R2
        1.1%
        26.2%
        50%
        48 bp
        GSM2837504
        4.5%
        30.9%
        51%
        47 bp
        104 bp
        6.3%
        73.7%
        133.5
        97.5%
        74.0
        43.4%
        5.9 X
        92.1
        6.3
        0.24
        GSM2837504_R2
        4.8%
        30.3%
        52%
        47 bp
        GSM2837505
        3.5%
        30.3%
        50%
        47 bp
        126 bp
        5.0%
        75.3%
        140.1
        97.2%
        72.7
        38.9%
        5.9 X
        91.7
        13.8
        0.19
        GSM2837505_R2
        3.8%
        29.7%
        51%
        47 bp
        GSM2837506
        0.6%
        38.1%
        50%
        49 bp
        127 bp
        8.9%
        75.6%
        201.4
        96.5%
        98.0
        35.3%
        8.2 X
        124.3
        28.0
        0.23
        GSM2837506_R2
        1.0%
        37.2%
        50%
        48 bp
        GSM2837507
        0.8%
        28.1%
        49%
        49 bp
        85 bp
        6.9%
        85.3%
        124.1
        97.3%
        79.6
        43.8%
        6.3 X
        94.6
        11.3
        0.22
        GSM2837507_R2
        1.0%
        28.0%
        49%
        49 bp
        GSM2837508
        6.0%
        37.3%
        49%
        46 bp
        69 bp
        19.8%
        86.6%
        243.0
        96.9%
        149.8
        38.2%
        11.6 X
        184.2
        26.1
        0.34
        GSM2837508_R2
        6.3%
        36.8%
        49%
        46 bp
        GSM4039581
        17.8%
        44.7%
        46%
        123 bp
        189 bp
        21.8%
        98.2%
        192.9
        98.5%
        122.7
        37.1%
        25.9 X
        152.2
        38.9
        0.38
        GSM4039581_R2
        17.9%
        39.6%
        46%
        123 bp
        GSM4039582
        24.4%
        36.2%
        46%
        115 bp
        171 bp
        11.2%
        98.4%
        98.2
        99.1%
        61.3
        37.0%
        12.0 X
        75.0
        22.4
        0.26
        GSM4039582_R2
        24.3%
        33.5%
        46%
        115 bp
        GSM4039583
        21.9%
        25.1%
        45%
        118 bp
        156 bp
        18.4%
        98.1%
        128.0
        99.1%
        97.8
        29.9%
        19.1 X
        118.9
        7.9
        0.29
        GSM4039583_R2
        21.9%
        11.4%
        45%
        118 bp
        GSM4039587
        26.3%
        20.0%
        44%
        110 bp
        123 bp
        17.0%
        98.3%
        148.2
        99.3%
        120.3
        27.5%
        21.8 X
        145.2
        1.7
        0.29
        GSM4039587_R2
        26.2%
        7.6%
        44%
        111 bp
        GSM4039588
        22.1%
        23.4%
        45%
        117 bp
        144 bp
        17.8%
        98.1%
        159.5
        99.2%
        131.1
        30.9%
        24.7 X
        155.1
        2.8
        0.32
        GSM4039588_R2
        21.9%
        14.8%
        45%
        117 bp
        GSM4039589
        23.8%
        28.6%
        47%
        114 bp
        157 bp
        21.5%
        97.8%
        177.4
        99.2%
        143.8
        38.9%
        26.3 X
        168.7
        6.2
        0.40
        GSM4039589_R2
        23.6%
        17.4%
        47%
        114 bp
        GSM4558119
        27.8%
        54.3%
        46%
        108 bp
        114 bp
        27.4%
        96.4%
        162.9
        97.4%
        69.6
        32.2%
        13.3 X
        90.6
        67.2
        0.37
        GSM4558119_R2
        26.4%
        46.9%
        47%
        110 bp
        GSM4558120
        23.7%
        42.8%
        48%
        114 bp
        163 bp
        13.3%
        98.0%
        140.0
        98.6%
        82.0
        42.4%
        15.8 X
        100.0
        38.0
        0.33
        GSM4558120_R2
        23.4%
        37.3%
        48%
        115 bp
        GSM4558121
        21.2%
        32.6%
        48%
        118 bp
        159 bp
        22.5%
        98.3%
        168.1
        98.5%
        127.2
        48.7%
        24.3 X
        151.3
        15.4
        0.43
        GSM4558121_R2
        21.2%
        18.6%
        48%
        118 bp
        GSM4558122
        27.5%
        33.3%
        49%
        109 bp
        123 bp
        18.7%
        97.9%
        154.6
        98.9%
        120.2
        57.9%
        20.7 X
        138.9
        13.4
        0.44
        GSM4558122_R2
        27.0%
        26.3%
        49%
        110 bp
        GSM4558123
        24.4%
        32.3%
        48%
        113 bp
        132 bp
        20.6%
        98.1%
        181.6
        98.6%
        139.1
        45.8%
        25.5 X
        165.4
        14.1
        0.40
        GSM4558123_R2
        24.4%
        30.1%
        48%
        113 bp
        GSM4558124
        26.1%
        31.2%
        49%
        111 bp
        124 bp
        22.2%
        98.2%
        149.7
        98.8%
        122.0
        55.5%
        21.2 X
        140.4
        7.7
        0.46
        GSM4558124_R2
        26.1%
        28.7%
        49%
        111 bp
        GSM4558125
        24.9%
        35.7%
        49%
        113 bp
        131 bp
        24.4%
        98.3%
        184.0
        98.6%
        151.4
        59.8%
        26.4 X
        172.3
        9.9
        0.51
        GSM4558125_R2
        24.8%
        33.1%
        49%
        113 bp
        GSM4558126
        33.5%
        25.8%
        49%
        100 bp
        88 bp
        18.7%
        96.7%
        138.9
        97.4%
        110.5
        63.0%
        17.2 X
        125.7
        9.3
        0.44
        GSM4558126_R2
        31.9%
        21.5%
        49%
        102 bp
        GSM4558127
        26.6%
        34.7%
        48%
        110 bp
        127 bp
        21.5%
        97.2%
        182.5
        98.9%
        137.4
        50.5%
        24.4 X
        161.4
        17.1
        0.42
        GSM4558127_R2
        26.6%
        23.7%
        48%
        110 bp
        GSM4558128
        28.0%
        32.0%
        48%
        108 bp
        111 bp
        19.7%
        98.1%
        170.1
        98.8%
        133.4
        47.3%
        22.9 X
        155.2
        12.7
        0.41
        GSM4558128_R2
        27.9%
        22.0%
        48%
        108 bp
        GSM4558129
        27.4%
        24.4%
        46%
        109 bp
        144 bp
        17.3%
        98.0%
        164.0
        99.0%
        134.4
        39.1%
        23.4 X
        157.5
        4.3
        0.36
        GSM4558129_R2
        27.4%
        16.5%
        47%
        109 bp
        GSM4558130
        34.9%
        12.9%
        46%
        98 bp
        87 bp
        14.5%
        96.8%
        131.7
        99.2%
        99.9
        21.6%
        16.7 X
        125.3
        3.0
        0.25
        GSM4558130_R2
        34.8%
        10.1%
        46%
        98 bp

        FastQC (raw)

        FastQC (raw) is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

        Sequence Length Distribution

        All samples have sequences of a single length (75bp , 49bp , 50bp , 51bp , 150bp). See the General Statistics Table.

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

        Overrepresented sequences

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as over represented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all of the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        loading..

        Cutadapt

        Cutadapt is a tool to find and remove adapter sequences, primers, poly-Atails and other types of unwanted sequence from your high-throughput sequencing reads.

        Filtered Reads

        This plot shows the number of reads (SE) / pairs (PE) removed by Cutadapt.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

        Trimmed Sequence Lengths

        This plot shows the number of reads with certain lengths of adapter trimmed.

        Obs/Exp shows the raw counts divided by the number expected due to sequencing errors. A defined peak may be related to adapter length.

        See the cutadapt documentation for more information on how these numbers are generated.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

        FastQC (trimmed)

        FastQC (trimmed) is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

        Overrepresented sequences

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as over represented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all of the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        112 samples had less than 1% of reads made up of overrepresented sequences

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        No samples found with any adapter contamination > 0.1%

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        loading..

        Picard

        Picard is a set of Java command line tools for manipulating high-throughput sequencing data.

        Insert Size

        Plot shows the number of reads at a given insert size. Reads with different orientations are summed.

        loading..

        Mark Duplicates

        Number of reads, categorised by duplication state. Pair counts are doubled - see help text for details.

        The table in the Picard metrics file contains some columns referring read pairs and some referring to single reads.

        To make the numbers in this plot sum correctly, values referring to pairs are doubled according to the scheme below:

        • READS_IN_DUPLICATE_PAIRS = 2 * READ_PAIR_DUPLICATES
        • READS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)
        • READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICAL
        • READS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATES
        • READS_UNMAPPED = UNMAPPED_READS
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        SamTools pre-sieve

        Samtools is a suite of programs for interacting with high-throughput sequencing data.

        The pre-sieve statistics are quality metrics measured before applying (optional) minimum mapping quality, blacklist removal, mitochondrial read removal, and tn5 shift.

        Percent Mapped

        Alignment metrics from samtools stats; mapped vs. unmapped reads.

        For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

        Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

        loading..

        Alignment metrics

        This module parses the output from samtools stats. All numbers in millions.

        loading..

        SamTools post-sieve

        Samtools is a suite of programs for interacting with high-throughput sequencing data.

        The post-sieve statistics are quality metrics measured after applying (optional) minimum mapping quality, blacklist removal, mitochondrial read removal, and tn5 shift.

        Percent Mapped

        Alignment metrics from samtools stats; mapped vs. unmapped reads.

        For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

        Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

        loading..

        Alignment metrics

        This module parses the output from samtools stats. All numbers in millions.

        loading..

        deepTools

        deepTools is a suite of tools to process and analyze deep sequencing data.

        Correlation heatmap

        Pairwise correlations of samples based on distribution of sequence reads

        loading..

        PCA plot

        PCA plot with the top two principal components calculated based on genome-wide distribution of sequence reads

        loading..

        Fingerprint plot

        Signal fingerprint according to plotFingerprint

        loading..

        Read Distribution Profile after Annotation

        Accumulated view of the distribution of sequence reads related to the closest annotated gene. All annotated genes have been normalized to the same size.

        • Green: -2.0Kb upstream of gene to TSS
        • Yellow: TSS to TES
        • Pink: TES to 0.5Kb downstream of gene
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        macs2_frips

        Subread featureCounts is a highly efficient general-purpose read summarization program that counts mapped reads for genomic features such as genes, exons, promoter, gene bodies, genomic bins and chromosomal locations.

        loading..

        Peak feature distribution (macs2)

        Figure generated by chipseeker

        Distribution of peak locations relative to TSS (macs2)

        Figure generated by chipseeker

        Samples & Config

        The samples file used for this run:

        sample assembly condition replicate hpf descriptive_name control study
        TShield_1 ASM318616v1 shield TShield_1 27.0 shield_rep1 unpublished_tena_skarmeta
        TShield_2 ASM318616v1 shield TShield_2 27.0 shield_rep2 unpublished_tena_skarmeta
        T80e_1 ASM318616v1 80epiboly T80e_1 32.0 80epiboly_rep1 unpublished_tena_skarmeta
        T80e_2 ASM318616v1 80epiboly T80e_2 32.0 80epiboly_rep2 unpublished_tena_skarmeta
        T8som_1 ASM318616v1 8som T8som_1 46.0 8som_rep1 unpublished_tena_skarmeta
        T8som_2 ASM318616v1 8som T8som_2 46.0 8som_rep2 unpublished_tena_skarmeta
        TPhylo_1 ASM318616v1 phylo TPhylo_1 72.0 phylo_rep1 unpublished_tena_skarmeta
        TPhylo_2 ASM318616v1 phylo TPhylo_2 72.0 phylo_rep2 unpublished_tena_skarmeta
        SF80e_1 Astyanax_mexicanus-2.0 80epiboly SF80e_1 8.0 80epiboly_rep1 unpublished_tena_skarmeta
        SF80e_2 Astyanax_mexicanus-2.0 80epiboly SF80e_2 8.0 80epiboly_rep2 unpublished_tena_skarmeta
        SF80e_3 Astyanax_mexicanus-2.0 80epiboly SF80e_3 8.0 80epiboly_rep3 unpublished_tena_skarmeta
        SF5ss_1 Astyanax_mexicanus-2.0 5somites SF5ss_1 12.0 5somites_rep1 unpublished_tena_skarmeta
        SF5ss_2 Astyanax_mexicanus-2.0 5somites SF5ss_2 12.0 5somites_rep2 unpublished_tena_skarmeta
        SF24hpf_2 Astyanax_mexicanus-2.0 24hpf SF24hpf_2 24.0 24hpf_rep1 unpublished_tena_skarmeta
        SF24hpf_3 Astyanax_mexicanus-2.0 24hpf SF24hpf_3 24.0 24hpf_rep2 unpublished_tena_skarmeta
        SF48hpf_2 Astyanax_mexicanus-2.0 48hpf SF48hpf_2 48.0 48hpf_rep1 unpublished_tena_skarmeta
        SF48hpf_3 Astyanax_mexicanus-2.0 48hpf SF48hpf_3 48.0 48hpf_rep2 unpublished_tena_skarmeta
        GSM2837480 Bl71nemr 15hpf GSM2837480 15.0 15h_rep1 https://doi.org/10.1038/s41586-018-0734-6
        GSM2837481 Bl71nemr 15hpf GSM2837481 15.0 15h_rep2 https://doi.org/10.1038/s41586-018-0734-6
        GSM2837482 Bl71nemr 36hpf GSM2837482 36.0 36h_rep1 https://doi.org/10.1038/s41586-018-0734-6
        GSM2837483 Bl71nemr 60hpf GSM2837483 60.0 60h_rep1 https://doi.org/10.1038/s41586-018-0734-6
        GSM2837484 Bl71nemr 60hpf GSM2837484 60.0 60h_rep2 https://doi.org/10.1038/s41586-018-0734-6
        GSM2837485 Bl71nemr 8hpf GSM2837485 8.0 8h_rep1 https://doi.org/10.1038/s41586-018-0734-6
        GSM2837486 Bl71nemr 8hpf GSM2837486 8.0 8h_rep2 https://doi.org/10.1038/s41586-018-0734-6
        GSM2385309 ce11 early_embryo GSM2385309 0.0 early_embryo_rep1 GSM2385318 http://www.genome.org/cgi/doi/10.1101/gr.226233.117
        GSM2385310 ce11 larval_st3 GSM2385310 36.0 larval_st3_rep1 GSM2385318 http://www.genome.org/cgi/doi/10.1101/gr.226233.117
        GSM2385311 ce11 young_adult GSM2385311 57.0 young_adult_rep1 GSM2385318 http://www.genome.org/cgi/doi/10.1101/gr.226233.117
        GSM2385312 ce11 early_embryo GSM2385312 0.0 early_embryo_rep2 GSM2385318 http://www.genome.org/cgi/doi/10.1101/gr.226233.117
        GSM2385313 ce11 larval_st3 GSM2385313 36.0 larval_st3_rep2 GSM2385318 http://www.genome.org/cgi/doi/10.1101/gr.226233.117
        GSM2385314 ce11 young_adult GSM2385314 57.0 young_adult_rep2 GSM2385318 http://www.genome.org/cgi/doi/10.1101/gr.226233.117
        GSM2385315 ce11 early_embryo GSM2385315 0.0 early_embryo_rep3 GSM2385318 http://www.genome.org/cgi/doi/10.1101/gr.226233.117
        GSM2385316 ce11 larval_st3 GSM2385316 36.0 larval_st3_rep3 GSM2385318 http://www.genome.org/cgi/doi/10.1101/gr.226233.117
        GSM2385317 ce11 young_adult GSM2385317 57.0 young_adult_rep3 GSM2385318 http://www.genome.org/cgi/doi/10.1101/gr.226233.117
        GSM3141722 ce11 L1 GSM3141722 4.0 L1_4h_rep1 https://doi.org/10.7554/elife.37344
        GSM3141723 ce11 L1 GSM3141723 4.0 L1_4h_rep2 https://doi.org/10.7554/elife.37344
        GSM3141724 ce11 L2 GSM3141724 20.0 L2_20h_rep1 https://doi.org/10.7554/elife.37344
        GSM3141725 ce11 L2 GSM3141725 20.0 L2_20h_rep2 https://doi.org/10.7554/elife.37344
        GSM3141726 ce11 L3 GSM3141726 30.0 L3_30h_rep1 https://doi.org/10.7554/elife.37344
        GSM3141727 ce11 L3 GSM3141727 30.0 L3_30h_rep2 https://doi.org/10.7554/elife.37344
        GSM3141728 ce11 L4 GSM3141728 45.0 L4_45h_rep1 https://doi.org/10.7554/elife.37344
        GSM3141729 ce11 L4 GSM3141729 45.0 L4_45h_rep2 https://doi.org/10.7554/elife.37344
        GSM3141730 ce11 YA GSM3141730 60.0 YoungAdult_60h_rep1 https://doi.org/10.7554/elife.37344
        GSM3141731 ce11 YA GSM3141731 60.0 YoungAdult_60h_rep2 https://doi.org/10.7554/elife.37344
        GSM3756599 danRer11 256cell GSM3756599 2.5 256cell_rep1 https://doi.org/10.1371/journal.pgen.1008546
        GSM3756600 danRer11 256cell GSM3756600 2.5 256cell_rep2 https://doi.org/10.1371/journal.pgen.1008546
        GSM3756606 danRer11 high GSM3756606 3.3 high_rep1 https://doi.org/10.1371/journal.pgen.1008546
        GSM3756607 danRer11 high GSM3756607 3.3 high_rep2 https://doi.org/10.1371/journal.pgen.1008546
        GSM3756608 danRer11 high GSM3756608 3.3 high_rep3 https://doi.org/10.1371/journal.pgen.1008546
        GSM3756609 danRer11 oblong GSM3756609 3.6 oblong_rep1 https://doi.org/10.1371/journal.pgen.1008546
        GSM3756610 danRer11 oblong GSM3756610 3.6 oblong_rep2 https://doi.org/10.1371/journal.pgen.1008546
        GSM3756611 danRer11 oblong GSM3756611 3.6 oblong_rep3 https://doi.org/10.1371/journal.pgen.1008546
        GSM3756614 danRer11 sphere GSM3756614 4.0 sphere_rep1 https://doi.org/10.1371/journal.pgen.1008546
        GSM3756615 danRer11 sphere GSM3756615 4.0 sphere_rep2 https://doi.org/10.1371/journal.pgen.1008546
        GSM2837495 danRer11 dome GSM2837495 4.3 dome_rep1 https://doi.org/10.1038/s41586-018-0734-6
        GSM2837497 danRer11 dome GSM2837497 4.3 dome_rep2 https://doi.org/10.1038/s41586-018-0734-6
        GSM3756603 danRer11 dome GSM3756603 4.3 dome_rep3 https://doi.org/10.1371/journal.pgen.1008546
        GSM3756604 danRer11 dome GSM3756604 4.3 dome_rep4 https://doi.org/10.1371/journal.pgen.1008546
        GSM3756605 danRer11 dome GSM3756605 4.3 dome_rep5 https://doi.org/10.1371/journal.pgen.1008546
        GSM2837496 danRer11 shield GSM2837496 6.0 shield_rep1 https://doi.org/10.1038/s41586-018-0734-6
        GSM2837498 danRer11 shield GSM2837498 6.0 shield_rep2 https://doi.org/10.1038/s41586-018-0734-6
        GSM3756612 danRer11 shield GSM3756612 6.0 shield_rep3 https://doi.org/10.1371/journal.pgen.1008546
        GSM3756613 danRer11 shield GSM3756613 6.0 shield_rep4 https://doi.org/10.1371/journal.pgen.1008546
        GSM2837491 danRer11 80epiboly GSM2837491 8.5 80epiboly_rep1 https://doi.org/10.1038/s41586-018-0734-6
        GSM2837492 danRer11 80epiboly GSM2837492 8.5 80epiboly_rep2 https://doi.org/10.1038/s41586-018-0734-6
        GSM3756601 danRer11 80epiboly GSM3756601 8.5 80epiboly_rep3 https://doi.org/10.1371/journal.pgen.1008546
        GSM3756602 danRer11 80epiboly GSM3756602 8.5 80epiboly_rep4 https://doi.org/10.1371/journal.pgen.1008546
        GSM2837493 danRer11 8somites GSM2837493 13.0 8somites_rep1 https://doi.org/10.1038/s41586-018-0734-6
        GSM2837494 danRer11 8somites GSM2837494 13.0 8somites_rep2 https://doi.org/10.1038/s41586-018-0734-6
        GSM3494511 danRer11 8somites GSM3494511 13.0 8somites_rep3 https://doi.org/10.1038/s41467-019-11121-z
        GSM3494510 danRer11 8somites GSM3494510 13.0 8omites_rep4 https://doi.org/10.1038/s41467-019-11121-z
        GSM3494509 danRer11 36hpf GSM3494509 36.0 36hpf_rep1 https://doi.org/10.1038/s41467-019-11121-z
        GSM3494508 danRer11 36hpf GSM3494508 36.0 36hpf_rep2 https://doi.org/10.1038/s41467-019-11121-z
        GSM2837489 danRer11 48hpf GSM2837489 48.0 48hpf_rep1 https://doi.org/10.1038/s41586-018-0734-6
        GSM2837490 danRer11 48hpf GSM2837490 48.0 48hpf_rep2 https://doi.org/10.1038/s41586-018-0734-6
        GSM2219678 dm6 st4_NC11 st4_NC11_03 1 st4_NC11_03_rep1
        GSM2219679 dm6 st4_NC11 st4_NC11_03 1 st4_NC11_03_rep2
        GSM2219680 dm6 st4_NC11 st4_NC11_03 1 st4_NC11_03_rep3
        GSM2219681 dm6 st4_NC11 st4_NC11_06 1 st4_NC11_06_rep1
        GSM2219682 dm6 st4_NC11 st4_NC11_06 1 st4_NC11_06_rep2
        GSM2219683 dm6 st4_NC11 st4_NC11_06 1 st4_NC11_06_rep3
        GSM2219684 dm6 st4_NC11 st4_NC11_09 1 st4_NC11_09_rep1
        GSM2219685 dm6 st4_NC11 st4_NC11_09 1 st4_NC11_09_rep2
        GSM2219686 dm6 st4_NC11 st4_NC11_09 1 st4_NC11_09_rep3
        GSM2219687 dm6 st4_NC12 st4_NC12_03 2 st4_NC12_03_rep1
        GSM2219688 dm6 st4_NC12 st4_NC12_03 2 st4_NC12_03_rep2
        GSM2219689 dm6 st4_NC12 st4_NC12_03 2 st4_NC12_03_rep3
        GSM2219690 dm6 st4_NC12 st4_NC12_06 2 st4_NC12_06_rep1
        GSM2219691 dm6 st4_NC12 st4_NC12_06 2 st4_NC12_06_rep2
        GSM2219692 dm6 st4_NC12 st4_NC12_06 2 st4_NC12_06_rep3
        GSM2219693 dm6 st4_NC12 st4_NC12_09 2 st4_NC12_09_rep1
        GSM2219694 dm6 st4_NC12 st4_NC12_09 2 st4_NC12_09_rep2
        GSM2219695 dm6 st4_NC12 st4_NC12_09 2 st4_NC12_09_rep3
        GSM2219696 dm6 st4_NC12 st4_NC12_12 2 st4_NC12_12_rep1
        GSM2219697 dm6 st4_NC12 st4_NC12_12 2 st4_NC12_12_rep2
        GSM2219698 dm6 st4_NC12 st4_NC12_12 2 st4_NC12_12_rep3
        GSM2219699 dm6 st4_NC12 st4_NC12_12 2 st4_NC12_12_rep4
        GSM2219700 dm6 st4_NC13 st4_NC13_03 3 st4_NC13_03_rep1
        GSM2219701 dm6 st4_NC13 st4_NC13_03 3 st4_NC13_03_rep2
        GSM2219702 dm6 st4_NC13 st4_NC13_03 3 st4_NC13_03_rep3
        GSM2219703 dm6 st4_NC13 st4_NC13_06 3 st4_NC13_06_rep1
        GSM2219704 dm6 st4_NC13 st4_NC13_06 3 st4_NC13_06_rep2
        GSM2219705 dm6 st4_NC13 st4_NC13_06 3 st4_NC13_06_rep3
        GSM2219706 dm6 st4_NC13 st4_NC13_09 3 st4_NC13_09_rep1
        GSM2219707 dm6 st4_NC13 st4_NC13_09 3 st4_NC13_09_rep2
        GSM2219708 dm6 st4_NC13 st4_NC13_09 3 st4_NC13_09_rep3
        GSM2219709 dm6 st4_NC13 st4_NC13_12 3 st4_NC13_12_rep1
        GSM2219710 dm6 st4_NC13 st4_NC13_12 3 st4_NC13_12_rep2
        GSM2219711 dm6 st4_NC13 st4_NC13_12 3 st4_NC13_12_rep3
        GSM2219712 dm6 st4_NC13 st4_NC13_15 3 st4_NC13_15_rep1
        GSM2219713 dm6 st4_NC13 st4_NC13_15 3 st4_NC13_15_rep2
        GSM2219714 dm6 st4_NC13 st4_NC13_15 3 st4_NC13_15_rep3
        GSM2219715 dm6 st4_NC13 st4_NC13_18 3 st4_NC13_18_rep1
        GSM2219716 dm6 st4_NC13 st4_NC13_18 3 st4_NC13_18_rep2
        GSM2219717 dm6 st4_NC13 st4_NC13_18 3 st4_NC13_18_rep3
        GSM2811115 dm6 st5 st5_anterior 4 st5_anterior
        GSM2811116 dm6 st5 st5_posterior 4 st5_posterior
        GSM2811117 dm6 st5 st5_whole 4 st5_whole
        GSM2811118 dm6 st5 st5_anterior 4 st5_anterior
        GSM2811119 dm6 st5 st5_posterior 4 st5_posterior
        GSM2811120 dm6 st5 st5_mixed 4 st5_mixed
        GSM2811121 dm6 st5 st5_whole 4 st5_whole
        GSM2285339 dm6 st7 GSM2285339 5 st7_rep1
        GSM2285340 dm6 st7 GSM2285340 5 st7_rep2
        DRR138947 GRCg6a HH6 DRR138947 24.0 HH6_rep1 https://doi.org/10.1186/s40851-019-0148-9
        DRR138948 GRCg6a HH6 DRR138948 24.0 HH6_rep2 https://doi.org/10.1186/s40851-019-0148-9
        DRR138949 GRCg6a HH6 DRR138949 24.0 HH6_rep3 https://doi.org/10.1186/s40851-019-0148-9
        DRR138950 GRCg6a HH11 DRR138950 42.5 HH11_rep1 https://doi.org/10.1186/s40851-019-0148-9
        DRR138951 GRCg6a HH11 DRR138951 42.5 HH11_rep2 https://doi.org/10.1186/s40851-019-0148-9
        DRR138952 GRCg6a HH11 DRR138952 42.5 HH11_rep3 https://doi.org/10.1186/s40851-019-0148-9
        DRR138953 GRCg6a HH16 DRR138953 53.5 HH16_rep1 https://doi.org/10.1186/s40851-019-0148-9
        DRR138954 GRCg6a HH16 DRR138954 53.5 HH16_rep2 https://doi.org/10.1186/s40851-019-0148-9
        DRR138955 GRCg6a HH16 DRR138955 53.5 HH16_rep3 https://doi.org/10.1186/s40851-019-0148-9
        DRR138956 GRCg6a HH19 DRR138956 78.0 HH19_rep1 https://doi.org/10.1186/s40851-019-0148-9
        DRR138957 GRCg6a HH19 DRR138957 78.0 HH19_rep2 https://doi.org/10.1186/s40851-019-0148-9
        DRR138958 GRCg6a HH19 DRR138958 78.0 HH19_rep3 https://doi.org/10.1186/s40851-019-0148-9
        DRR138959 GRCg6a HH24 DRR138959 108.0 HH24_rep1 https://doi.org/10.1186/s40851-019-0148-9
        DRR138960 GRCg6a HH24 DRR138960 108.0 HH24_rep2 https://doi.org/10.1186/s40851-019-0148-9
        DRR138961 GRCg6a HH24 DRR138961 108.0 HH24_rep3 https://doi.org/10.1186/s40851-019-0148-9
        DRR138962 GRCg6a HH28 DRR138962 138.0 HH28_rep1 https://doi.org/10.1186/s40851-019-0148-9
        DRR138963 GRCg6a HH28 DRR138963 138.0 HH28_rep2 https://doi.org/10.1186/s40851-019-0148-9
        DRR138964 GRCg6a HH28 DRR138964 138.0 HH28_rep3 https://doi.org/10.1186/s40851-019-0148-9
        DRR138965 GRCg6a HH32 DRR138965 180.0 HH32_rep1 https://doi.org/10.1186/s40851-019-0148-9
        DRR138966 GRCg6a HH32 DRR138966 180.0 HH32_rep2 https://doi.org/10.1186/s40851-019-0148-9
        DRR138967 GRCg6a HH32 DRR138967 180.0 HH32_rep3 https://doi.org/10.1186/s40851-019-0148-9
        DRR138968 GRCg6a HH38 DRR138968 288.0 HH38_rep1 https://doi.org/10.1186/s40851-019-0148-9
        DRR138969 GRCg6a HH38 DRR138969 288.0 HH38_rep2 https://doi.org/10.1186/s40851-019-0148-9
        DRR138970 GRCg6a HH38 DRR138970 288.0 HH38_rep3 https://doi.org/10.1186/s40851-019-0148-9
        DRR138923 mm10 st7.5 DRR138923 180.0 st7.5_rep1 https://doi.org/10.1186/s40851-019-0148-9
        DRR138924 mm10 st7.5 DRR138924 180.0 st7.5_rep2 https://doi.org/10.1186/s40851-019-0148-9
        DRR138925 mm10 st7.5 DRR138925 180.0 st7.5_rep3 https://doi.org/10.1186/s40851-019-0148-9
        DRR138926 mm10 st8.5 DRR138926 204.0 st8.5_rep1 https://doi.org/10.1186/s40851-019-0148-9
        DRR138927 mm10 st8.5 DRR138927 204.0 st8.5_rep2 https://doi.org/10.1186/s40851-019-0148-9
        DRR138928 mm10 st8.5 DRR138928 204.0 st8.5_rep3 https://doi.org/10.1186/s40851-019-0148-9
        DRR138929 mm10 st9.5 DRR138929 228.0 st9.5_rep1 https://doi.org/10.1186/s40851-019-0148-9
        DRR138930 mm10 st9.5 DRR138930 228.0 st9.5_rep2 https://doi.org/10.1186/s40851-019-0148-9
        DRR138931 mm10 st9.5 DRR138931 228.0 st9.5_rep3 https://doi.org/10.1186/s40851-019-0148-9
        DRR138932 mm10 st10.5 DRR138932 252.0 st10.5_rep1 https://doi.org/10.1186/s40851-019-0148-9
        DRR138933 mm10 st10.5 DRR138933 252.0 st10.5_rep2 https://doi.org/10.1186/s40851-019-0148-9
        DRR138934 mm10 st10.5 DRR138934 252.0 st10.5_rep3 https://doi.org/10.1186/s40851-019-0148-9
        DRR138935 mm10 st12.5 DRR138935 300.0 st12.5_rep1 https://doi.org/10.1186/s40851-019-0148-9
        DRR138936 mm10 st12.5 DRR138936 300.0 st12.5_rep2 https://doi.org/10.1186/s40851-019-0148-9
        DRR138937 mm10 st12.5 DRR138937 300.0 st12.5_rep3 https://doi.org/10.1186/s40851-019-0148-9
        DRR138938 mm10 st14.5 DRR138938 348.0 st14.5_rep1 https://doi.org/10.1186/s40851-019-0148-9
        DRR138939 mm10 st14.5 DRR138939 348.0 st14.5_rep2 https://doi.org/10.1186/s40851-019-0148-9
        DRR138992 mm10 st14.5 DRR138992 348.0 st14.5_rep3 https://doi.org/10.1186/s40851-019-0148-9
        DRR138940 mm10 st14.5 DRR138940 348.0 st14.5_rep4 https://doi.org/10.1186/s40851-019-0148-9
        DRR138941 mm10 st16.5 DRR138941 396.0 st16.5_rep1 https://doi.org/10.1186/s40851-019-0148-9
        DRR138942 mm10 st16.5 DRR138942 396.0 st16.5_rep2 https://doi.org/10.1186/s40851-019-0148-9
        DRR138943 mm10 st16.5 DRR138943 396.0 st16.5_rep3 https://doi.org/10.1186/s40851-019-0148-9
        DRR138944 mm10 st18.5 DRR138944 444.0 st18.5_rep1 https://doi.org/10.1186/s40851-019-0148-9
        DRR138993 mm10 st18.5 DRR138993 444.0 st18.5_rep2 https://doi.org/10.1186/s40851-019-0148-9
        DRR138945 mm10 st18.5 DRR138945 444.0 st18.5_rep3 https://doi.org/10.1186/s40851-019-0148-9
        DRR138946 mm10 st18.5 DRR138946 444.0 st18.5_rep4 https://doi.org/10.1186/s40851-019-0148-9
        DRR138994 mm10 st18.5 DRR138994 444.0 st18.5_rep5 https://doi.org/10.1186/s40851-019-0148-9
        GSM2837499 ASM223467v1 st11 GSM2837499 8.25 st11_rep1 https://doi.org/10.1038/s41586-018-0734-6
        GSM2837500 ASM223467v1 st11 GSM2837500 8.25 st11_rep2 https://doi.org/10.1038/s41586-018-0734-6
        GSM4039581 ASM223467v1 st11 GSM4039581 8.25 st11_rep3 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136027
        GSM4558119 ASM223467v1 st11 GSM4558119 8.25 st11_rep4 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136027
        GSM2837501 ASM223467v1 st13 GSM2837501 13.0 st13_rep1 https://doi.org/10.1038/s41586-018-0734-6
        GSM2837502 ASM223467v1 st13 GSM2837502 13.0 st13_rep2 https://doi.org/10.1038/s41586-018-0734-6
        DRR138971 ASM223467v1 st15 DRR138971 17.5 st15_rep1 https://doi.org/10.1186/s40851-019-0148-9
        DRR138972 ASM223467v1 st15 DRR138972 17.5 st15_rep2 https://doi.org/10.1186/s40851-019-0148-9
        DRR138973 ASM223467v1 st15 DRR138973 17.5 st15_rep3 https://doi.org/10.1186/s40851-019-0148-9
        GSM4039582 ASM223467v1 st15 GSM4039582 17.5 st15_rep4 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136027
        GSM4558120 ASM223467v1 st15 GSM4558120 17.5 st15_rep5 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136027
        GSM2837503 ASM223467v1 st19 GSM2837503 27.5 st19_rep1 https://doi.org/10.1038/s41586-018-0734-6
        GSM2837504 ASM223467v1 st19 GSM2837504 27.5 st19_rep2 https://doi.org/10.1038/s41586-018-0734-6
        GSM4039583 ASM223467v1 st19 GSM4039583 27.5 st19_rep3 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136027
        GSM4558121 ASM223467v1 st19 GSM4558121 27.5 st19_rep4 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136027
        DRR138974 ASM223467v1 st21 DRR138974 34.0 st21_rep1 https://doi.org/10.1186/s40851-019-0148-9
        DRR138975 ASM223467v1 st21 DRR138975 34.0 st21_rep2 https://doi.org/10.1186/s40851-019-0148-9
        DRR138976 ASM223467v1 st21 DRR138976 34.0 st21_rep3 https://doi.org/10.1186/s40851-019-0148-9
        DRR138995 ASM223467v1 st21 DRR138995 34.0 st21_rep4 https://doi.org/10.1186/s40851-019-0148-9
        DRR138996 ASM223467v1 st21 DRR138996 34.0 st21_rep5 https://doi.org/10.1186/s40851-019-0148-9
        DRR138997 ASM223467v1 st21 DRR138997 34.0 st21_rep6 https://doi.org/10.1186/s40851-019-0148-9
        DRR138977 ASM223467v1 st24 DRR138977 44.0 st24_rep1 https://doi.org/10.1186/s40851-019-0148-9
        DRR138978 ASM223467v1 st24 DRR138978 44.0 st24_rep2 https://doi.org/10.1186/s40851-019-0148-9
        DRR138979 ASM223467v1 st24 DRR138979 44.0 st24_rep3 https://doi.org/10.1186/s40851-019-0148-9
        GSM2837505 ASM223467v1 st25 GSM2837505 50.0 st25_rep1 https://doi.org/10.1038/s41586-018-0734-6
        GSM2837506 ASM223467v1 st25 GSM2837506 50.0 st25_rep2 https://doi.org/10.1038/s41586-018-0734-6
        GSM4558122 ASM223467v1 st25 GSM4558122 50.0 st25_rep3 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136027
        GSM4558123 ASM223467v1 st25 GSM4558123 50.0 st25_rep4 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136027
        DRR138980 ASM223467v1 st28 DRR138980 64.0 st28_rep1 https://doi.org/10.1186/s40851-019-0148-9
        DRR138998 ASM223467v1 st28 DRR138998 64.0 st28_rep2 https://doi.org/10.1186/s40851-019-0148-9
        DRR138999 ASM223467v1 st28 DRR138999 64.0 st28_rep3 https://doi.org/10.1186/s40851-019-0148-9
        DRR138981 ASM223467v1 st28 DRR138981 64.0 st28_rep4 https://doi.org/10.1186/s40851-019-0148-9
        DRR138982 ASM223467v1 st28 DRR138982 64.0 st28_rep5 https://doi.org/10.1186/s40851-019-0148-9
        GSM4558124 ASM223467v1 st29 GSM4558124 74.0 st29_rep1 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136027
        GSM4558125 ASM223467v1 st29 GSM4558125 74.0 st29_rep2 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136027
        GSM2837507 ASM223467v1 st32 GSM2837507 101.0 st32_rep1 https://doi.org/10.1038/s41586-018-0734-6
        GSM2837508 ASM223467v1 st32 GSM2837508 101.0 st32_rep2 https://doi.org/10.1038/s41586-018-0734-6
        DRR138983 ASM223467v1 st32 DRR138983 101.0 st32_rep3 https://doi.org/10.1186/s40851-019-0148-9
        DRR138984 ASM223467v1 st32 DRR138984 101.0 st32_rep4 https://doi.org/10.1186/s40851-019-0148-9
        DRR138985 ASM223467v1 st32 DRR138985 101.0 st32_rep5 https://doi.org/10.1186/s40851-019-0148-9
        GSM4558126 ASM223467v1 st32 GSM4558126 101.0 st32_rep6 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136027
        GSM4558127 ASM223467v1 st32 GSM4558127 101.0 st32_rep7 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136027
        DRR138986 ASM223467v1 st36 DRR138986 144.0 st36_rep1 https://doi.org/10.1186/s40851-019-0148-9
        DRR138987 ASM223467v1 st36 DRR138987 144.0 st36_rep2 https://doi.org/10.1186/s40851-019-0148-9
        DRR138988 ASM223467v1 st36 DRR138988 144.0 st36_rep3 https://doi.org/10.1186/s40851-019-0148-9
        GSM4039587 ASM223467v1 st36 GSM4039587 144.0 st36_rep4 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136027
        GSM4558128 ASM223467v1 st36 GSM4558128 144.0 st36_rep5 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136027
        GSM4039588 ASM223467v1 st38 GSM4039588 192.0 st38_rep1 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136027
        GSM4558129 ASM223467v1 st38 GSM4558129 192.0 st38_rep2 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136027
        DRR138989 ASM223467v1 st40 DRR138989 216.0 st40_rep1 https://doi.org/10.1186/s40851-019-0148-9
        DRR138990 ASM223467v1 st40 DRR138990 216.0 st40_rep2 https://doi.org/10.1186/s40851-019-0148-9
        DRR139001 ASM223467v1 st40 DRR139001 216.0 st40_rep3 https://doi.org/10.1186/s40851-019-0148-9
        DRR138991 ASM223467v1 st40 DRR138991 216.0 st40_rep4 https://doi.org/10.1186/s40851-019-0148-9
        DRR139002 ASM223467v1 st40 DRR139002 216.0 st40_rep5 https://doi.org/10.1186/s40851-019-0148-9
        GSM4039589 ASM223467v1 st40 GSM4039589 216.0 st40_rep6 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136027
        GSM4558130 ASM223467v1 st40 GSM4558130 216.0 st40_rep7 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136027
        SRR7410984 Phmamm_MTP2014 16_cell_st SRR7410984 1 16_cell_st_rep1 https://doi.org/10.1016/j.ydbio.2019.01.003
        SRR7410983 Phmamm_MTP2014 16_cell_st SRR7410983 1 16_cell_st_rep2 https://doi.org/10.1016/j.ydbio.2019.01.003
        SRR7293257 Phmamm_MTP2014 32_cell_st SRR7293257 2 32_cell_st https://doi.org/10.1016/j.ydbio.2019.01.003
        SRR8247806 Phmamm_MTP2014 64_cell_st SRR8247806 3 64_cell_st_rep1 https://doi.org/10.1016/j.ydbio.2019.01.003
        SRR7293255 Phmamm_MTP2014 64_cell_st SRR7293255 3 64_cell_st_rep2 https://doi.org/10.1016/j.ydbio.2019.01.003
        SRR7293256 Phmamm_MTP2014 112_cell_st SRR7293256 4 112_cell_st_rep1 https://doi.org/10.1016/j.ydbio.2019.01.003
        SRR7293254 Phmamm_MTP2014 112_cell_st SRR7293254 4 112_cell_st_rep2 https://doi.org/10.1016/j.ydbio.2019.01.003
        SRR7410986 Phmamm_MTP2014 late_gastrula SRR7410986 5 late_gastrula_rep1 https://doi.org/10.1016/j.ydbio.2019.01.003
        SRR8420409 Phmamm_MTP2014 late_gastrula SRR8420409 5 late_gastrula_rep2 https://doi.org/10.1016/j.ydbio.2019.01.003
        SRR8420408 Phmamm_MTP2014 mid_neurula SRR8420408 6 mid_neurula_rep1 https://doi.org/10.1016/j.ydbio.2019.01.003
        SRR7410985 Phmamm_MTP2014 mid_neurula SRR7410985 6 mid_neurula_rep2 https://doi.org/10.1016/j.ydbio.2019.01.003
        GSM2520641 Spur_5.0 18hpf GSM2520641 18.0 18hpf_rep1 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95651
        GSM2520642 Spur_5.0 18hpf GSM2520642 18.0 18hpf_rep2 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95651
        GSM2520643 Spur_5.0 18hpf GSM2520643 18.0 18hpf_rep3 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95651
        GSM2520644 Spur_5.0 24hpf GSM2520644 24.0 24hpf_rep1 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95651
        GSM2520645 Spur_5.0 24hpf GSM2520645 24.0 24hpf_rep2 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95651
        GSM2520646 Spur_5.0 24hpf GSM2520646 24.0 24hpf_rep3 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95651
        GSM2546188 Spur_5.0 28hpf GSM2546188 28.0 28hpf_rep1 https://doi.org/10.1186/s12864-018-4542-z
        GSM2546189 Spur_5.0 28hpf GSM2546189 28.0 28hpf_rep2 https://doi.org/10.1186/s12864-018-4542-z
        GSM2546190 Spur_5.0 28hpf GSM2546190 28.0 28hpf_rep3 https://doi.org/10.1186/s12864-018-4542-z
        GSM2520647 Spur_5.0 30hpf GSM2520647 30.0 30hpf_rep1 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95651
        GSM2520648 Spur_5.0 30hpf GSM2520648 30.0 30hpf_rep2 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95651
        GSM2520649 Spur_5.0 30hpf GSM2520649 30.0 30hpf_rep3 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95651
        GSM2520650 Spur_5.0 39hpf GSM2520650 39.0 39hpf_rep1 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95651
        GSM2520651 Spur_5.0 39hpf GSM2520651 39.0 39hpf_rep2 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95651
        GSM2520652 Spur_5.0 39hpf GSM2520652 39.0 39hpf_rep3 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95651
        GSM2520653 Spur_5.0 50hpf GSM2520653 50.0 50hpf_rep1 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95651
        GSM2520654 Spur_5.0 50hpf GSM2520654 50.0 50hpf_rep2 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95651
        GSM2520655 Spur_5.0 50hpf GSM2520655 50.0 50hpf_rep3 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95651
        GSM2520656 Spur_5.0 60hpf GSM2520656 60.0 60hpf_rep1 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95651
        GSM2520657 Spur_5.0 60hpf GSM2520657 60.0 60hpf_rep2 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95651
        GSM2520658 Spur_5.0 60hpf GSM2520658 60.0 60hpf_rep3 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95651
        GSM2520659 Spur_5.0 70hpf GSM2520659 70.0 70hpf_rep1 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95651
        GSM2520660 Spur_5.0 70hpf GSM2520660 70.0 70hpf_rep2 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95651
        GSM2520661 Spur_5.0 70hpf GSM2520661 70.0 70hpf_rep3 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95651
        Xt_stage9_rep2 xenTro9 st9 Xt_stage9_rep2 7 st9_rep2 unpublished_internal; https://www.xenbase.org/anatomy/alldev.do
        Xt_stage105_rep2 xenTro9 st10.5 Xt_stage105_rep2 11 st10.5_rep2 unpublished_internal; https://www.xenbase.org/anatomy/alldev.do
        Xt_stage105_rep3 xenTro9 st10.5 Xt_stage105_rep3 11 st10.5_rep3 unpublished_internal; https://www.xenbase.org/anatomy/alldev.do
        Xt_stage12_rep1 xenTro9 st12 Xt_stage12_rep1 13.25 st12_rep1 unpublished_internal; https://www.xenbase.org/anatomy/alldev.do
        Xt_stage12_rep2 xenTro9 st12 Xt_stage12_rep2 13.25 st12_rep2 unpublished_internal; https://www.xenbase.org/anatomy/alldev.do
        Xt_stage16_rep2 xenTro9 st16 Xt_stage16_rep2 18.25 st16_rep2 unpublished_internal; https://www.xenbase.org/anatomy/alldev.do
        Xt_stage16_rep1 xenTro9 st16 Xt_stage16_rep1 18.25 st16_rep1 unpublished_internal; https://www.xenbase.org/anatomy/alldev.do
        Xt_stage26_rep1 xenTro9 st26 Xt_stage26_rep1 29.5 st26_rep1 unpublished_internal; https://www.xenbase.org/anatomy/alldev.do
        Xt_stage26_rep2 xenTro9 st26 Xt_stage26_rep2 29.5 st26_rep2 unpublished_internal; https://www.xenbase.org/anatomy/alldev.do
        Xt_stage34_rep1 xenTro9 st34 Xt_stage34_rep1 44.5 st34_rep1 unpublished_internal; https://www.xenbase.org/anatomy/alldev.do
        Xt_stage34_rep2 xenTro9 st34 Xt_stage34_rep2 44.5 st34_rep2 unpublished_internal; https://www.xenbase.org/anatomy/alldev.do
        GSM4121478 Xenopus_laevis_v2 st10_ect GSM4121478 9.0 st10_ect_rep1 https://doi.org/10.1016/j.ydbio.2020.02.013
        GSM4121479 Xenopus_laevis_v2 st10_ect GSM4121479 9.0 st10_ect_rep2 https://doi.org/10.1016/j.ydbio.2020.02.013
        GSM4121480 Xenopus_laevis_v2 st10_ect GSM4121480 9.0 st10_ect_rep3 https://doi.org/10.1016/j.ydbio.2020.02.013
        GSM4121481 Xenopus_laevis_v2 st12_ect GSM4121481 13.25 st12_ect_rep1 https://doi.org/10.1016/j.ydbio.2020.02.013
        GSM4121482 Xenopus_laevis_v2 st12_ect GSM4121482 13.25 st12_ect_rep2 https://doi.org/10.1016/j.ydbio.2020.02.013
        GSM4121483 Xenopus_laevis_v2 st12_ect GSM4121483 13.25 st12_ect_rep3 https://doi.org/10.1016/j.ydbio.2020.02.013
        SRR10680317 ARS-UCD1.2 GVoocyte SRR10680317 1 GVoocyte_rep1 https://www.biorxiv.org/content/10.1101/2019.12.12.874479v2.full.pdf
        SRR10680316 ARS-UCD1.2 GVoocyte SRR10680316 1 GVoocyte_rep2 https://www.biorxiv.org/content/10.1101/2019.12.12.874479v2.full.pdf
        SRR10680297 ARS-UCD1.2 GVoocyte SRR10680297 1 GVoocyte_rep3 https://www.biorxiv.org/content/10.1101/2019.12.12.874479v2.full.pdf
        SRR10680305 ARS-UCD1.2 2-cell SRR10680305 2 2-cell_rep1 https://www.biorxiv.org/content/10.1101/2019.12.12.874479v2.full.pdf
        SRR10680304 ARS-UCD1.2 2-cell SRR10680304 2 2-cell_rep2 https://www.biorxiv.org/content/10.1101/2019.12.12.874479v2.full.pdf
        SRR10680303 ARS-UCD1.2 2-cell SRR10680303 2 2-cell_rep3 https://www.biorxiv.org/content/10.1101/2019.12.12.874479v2.full.pdf
        SRR10680313 ARS-UCD1.2 4-cell SRR10680313 3 4-cell_rep1 https://www.biorxiv.org/content/10.1101/2019.12.12.874479v2.full.pdf
        SRR10680312 ARS-UCD1.2 4-cell SRR10680312 3 4-cell_rep2 https://www.biorxiv.org/content/10.1101/2019.12.12.874479v2.full.pdf
        SRR10680311 ARS-UCD1.2 4-cell SRR10680311 3 4-cell_rep3 https://www.biorxiv.org/content/10.1101/2019.12.12.874479v2.full.pdf
        SRR10680299 ARS-UCD1.2 8-cell SRR10680299 4 8-cell_rep1 https://www.biorxiv.org/content/10.1101/2019.12.12.874479v2.full.pdf
        SRR10680298 ARS-UCD1.2 8-cell SRR10680298 4 8-cell_rep2 https://www.biorxiv.org/content/10.1101/2019.12.12.874479v2.full.pdf
        SRR10680296 ARS-UCD1.2 8-cell SRR10680296 4 8-cell_rep3 https://www.biorxiv.org/content/10.1101/2019.12.12.874479v2.full.pdf
        SRR10680295 ARS-UCD1.2 morula SRR10680295 5 morula_rep1 https://www.biorxiv.org/content/10.1101/2019.12.12.874479v2.full.pdf
        SRR10680294 ARS-UCD1.2 morula SRR10680294 5 morula_rep2 https://www.biorxiv.org/content/10.1101/2019.12.12.874479v2.full.pdf
        SRR10680309 ARS-UCD1.2 morula SRR10680309 5 morula_rep3 https://www.biorxiv.org/content/10.1101/2019.12.12.874479v2.full.pdf

        The config file used for this run: 
        # tab-separated file of the samples
samples: samples.tsv

# pipeline file locations
result_dir: ./results  # where to store results
genome_dir: ./genomes  # where to look for or download the genomes
# fastq_dir: (default is inside result_dir) # where to look for or download the fastqs


# contact info for multiqc report and trackhub
email: yourmail@here.com

# produce a UCSC trackhub?
create_trackhub: True
#create_qc_report: False

# how to handle replicates
biological_replicates: fisher  # change to "keep" to not combine them
technical_replicates: merge    # change to "keep" to not combine them

# which aligner to use
aligner: bwa-mem2

# filtering after alignment
remove_blacklist: True
remove_mito: True
tn5_shift: True
min_mapping_quality: 30
only_primary_align: True

# peak callers (supported peak callers are macs2, and genrich)
peak_caller:
  macs2:
      --shift -100 --extsize 200 --nomodel --keep-dup 1 --buffer-size 10000
#  genrich:
#      -y -j -r

        MultiQC v1.9 - Written by Phil Ewels, available on GitHub.

        This report uses HighCharts, jQuery, jQuery UI, Bootstrap, FileSaver.js and clipboard.js.